MobileNetV2(
  (network): Sequential(
    (0): Sequential(
      (0): Conv2d(3, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True)
      (2): Dropout2d(p=0.2, inplace)
      (3): ReLU6(inplace)
    )
    (1): InvertedResidualBlock(
      (block): Sequential(
        (0): Conv2d(32, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True)
        (2): ReLU6(inplace)
        (3): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)
        (4): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True)
        (5): ReLU6(inplace)
        (6): Conv2d(32, 16, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (7): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True)
      )
    )
    (2): InvertedResidualBlock(
      (block): Sequential(
        (0): Conv2d(16, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (1): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True)
        (2): ReLU6(inplace)
        (3): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=96, bias=False)
        (4): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True)
        (5): ReLU6(inplace)
        (6): Conv2d(96, 24, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (7): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True)
      )
    )
    (3): InvertedResidualBlock(
      (block): Sequential(
        (0): Conv2d(24, 144, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (1): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True)
        (2): ReLU6(inplace)
        (3): Conv2d(144, 144, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=144, bias=False)
        (4): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True)
        (5): ReLU6(inplace)
        (6): Conv2d(144, 24, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (7): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True)
      )
    )
    (4): InvertedResidualBlock(
      (block): Sequential(
        (0): Conv2d(24, 144, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (1): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True)
        (2): ReLU6(inplace)
        (3): Conv2d(144, 144, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=144, bias=False)
        (4): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True)
        (5): ReLU6(inplace)
        (6): Conv2d(144, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (7): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True)
      )
    )
    (5): InvertedResidualBlock(
      (block): Sequential(
        (0): Conv2d(32, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True)
        (2): ReLU6(inplace)
        (3): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=192, bias=False)
        (4): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True)
        (5): ReLU6(inplace)
        (6): Conv2d(192, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (7): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True)
      )
    )
    (6): InvertedResidualBlock(
      (block): Sequential(
        (0): Conv2d(32, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True)
        (2): ReLU6(inplace)
        (3): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=192, bias=False)
        (4): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True)
        (5): ReLU6(inplace)
        (6): Conv2d(192, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (7): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True)
      )
    )
    (7): InvertedResidualBlock(
      (block): Sequential(
        (0): Conv2d(32, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True)
        (2): ReLU6(inplace)
        (3): Conv2d(192, 192, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=192, bias=False)
        (4): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True)
        (5): ReLU6(inplace)
        (6): Conv2d(192, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (7): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True)
      )
    )
    (8): InvertedResidualBlock(
      (block): Sequential(
        (0): Conv2d(64, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True)
        (2): ReLU6(inplace)
        (3): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=384, bias=False)
        (4): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True)
        (5): ReLU6(inplace)
        (6): Conv2d(384, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (7): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True)
      )
    )
    (9): InvertedResidualBlock(
      (block): Sequential(
        (0): Conv2d(64, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True)
        (2): ReLU6(inplace)
        (3): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=384, bias=False)
        (4): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True)
        (5): ReLU6(inplace)
        (6): Conv2d(384, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (7): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True)
      )
    )
    (10): InvertedResidualBlock(
      (block): Sequential(
        (0): Conv2d(64, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True)
        (2): ReLU6(inplace)
        (3): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=384, bias=False)
        (4): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True)
        (5): ReLU6(inplace)
        (6): Conv2d(384, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (7): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True)
      )
    )
    (11): InvertedResidualBlock(
      (block): Sequential(
        (0): Conv2d(64, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True)
        (2): ReLU6(inplace)
        (3): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=384, bias=False)
        (4): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True)
        (5): ReLU6(inplace)
        (6): Conv2d(384, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (7): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True)
      )
    )
    (12): InvertedResidualBlock(
      (block): Sequential(
        (0): Conv2d(96, 576, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True)
        (2): ReLU6(inplace)
        (3): Conv2d(576, 576, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=576, bias=False)
        (4): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True)
        (5): ReLU6(inplace)
        (6): Conv2d(576, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (7): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True)
      )
    )
    (13): InvertedResidualBlock(
      (block): Sequential(
        (0): Conv2d(96, 576, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True)
        (2): ReLU6(inplace)
        (3): Conv2d(576, 576, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=576, bias=False)
        (4): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True)
        (5): ReLU6(inplace)
        (6): Conv2d(576, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (7): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True)
      )
    )
    (14): InvertedResidualBlock(
      (block): Sequential(
        (0): Conv2d(96, 576, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True)
        (2): ReLU6(inplace)
        (3): Conv2d(576, 576, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=576, bias=False)
        (4): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True)
        (5): ReLU6(inplace)
        (6): Conv2d(576, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (7): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True)
      )
    )
    (15): InvertedResidualBlock(
      (block): Sequential(
        (0): Conv2d(160, 960, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True)
        (2): ReLU6(inplace)
        (3): Conv2d(960, 960, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=960, bias=False)
        (4): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True)
        (5): ReLU6(inplace)
        (6): Conv2d(960, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (7): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True)
      )
    )
    (16): InvertedResidualBlock(
      (block): Sequential(
        (0): Conv2d(160, 960, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True)
        (2): ReLU6(inplace)
        (3): Conv2d(960, 960, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=960, bias=False)
        (4): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True)
        (5): ReLU6(inplace)
        (6): Conv2d(960, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (7): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True)
      )
    )
    (17): InvertedResidualBlock(
      (block): Sequential(
        (0): Conv2d(160, 960, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True)
        (2): ReLU6(inplace)
        (3): Conv2d(960, 960, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=960, bias=False)
        (4): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True)
        (5): ReLU6(inplace)
        (6): Conv2d(960, 320, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (7): BatchNorm2d(320, eps=1e-05, momentum=0.1, affine=True)
      )
    )
    (18): Sequential(
      (0): Conv2d(320, 1280, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (1): BatchNorm2d(1280, eps=1e-05, momentum=0.1, affine=True)
      (2): Dropout2d(p=0.2, inplace)
      (3): ReLU6(inplace)
    )
    (19): Dropout2d(p=0.2, inplace)
    (20): AvgPool2d(kernel_size=(4, 4), stride=(4, 4), padding=0, ceil_mode=False, count_include_pad=True)
    (21): Dropout2d(p=0.2, inplace)
    (22): Conv2d(1280, 10, kernel_size=(1, 1), stride=(1, 1))
  )
)

Test Results | loss: 0.48920726870434195 - acc-top1: 0.89912- acc-top5: 0.99060